dspy.XMLAdapter

dspy.XMLAdapter(callbacks: list[BaseCallback] | None = None, use_native_function_calling: bool = False, native_response_types: list[type[type]] | None = None, use_json_adapter_fallback: bool = True, parallel_tool_calls: bool | None = None)

Bases: ChatAdapter

Parameters:

Name	Type	Description	Default
callbacks	list[BaseCallback] | None	List of callback functions to execute during adapter methods.	None
use_native_function_calling	bool	Whether to enable native function calling capabilities.	False
native_response_types	list[type[type]] | None	List of output field types handled by native LM features.	None
use_json_adapter_fallback	bool	Whether to automatically fallback to JSONAdapter if the ChatAdapter fails. If True, when an error occurs (except ContextWindowExceededError), the adapter will retry using JSONAdapter. Defaults to True.	True
parallel_tool_calls	bool | None	Whether to request provider-side parallel tool-call generation when native function calling is active. If None, the adapter does not set the provider option.	None

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/chat_adapter.py

def __init__(
    self,
    callbacks: list[BaseCallback] | None = None,
    use_native_function_calling: bool = False,
    native_response_types: list[type[type]] | None = None,
    use_json_adapter_fallback: bool = True,
    parallel_tool_calls: bool | None = None,
):
    """
    Args:
        callbacks: List of callback functions to execute during adapter methods.
        use_native_function_calling: Whether to enable native function calling capabilities.
        native_response_types: List of output field types handled by native LM features.
        use_json_adapter_fallback: Whether to automatically fallback to JSONAdapter if the ChatAdapter fails.
            If True, when an error occurs (except ContextWindowExceededError), the adapter will retry using
            JSONAdapter. Defaults to True.
        parallel_tool_calls: Whether to request provider-side parallel tool-call generation when native function
            calling is active. If None, the adapter does not set the provider option.
    """
    super().__init__(
        callbacks=callbacks,
        use_native_function_calling=use_native_function_calling,
        parallel_tool_calls=parallel_tool_calls,
        native_response_types=native_response_types,
    )
    self.use_json_adapter_fallback = use_json_adapter_fallback

Functions

__call__(lm: BaseLM, lm_kwargs: dict[str, Any], signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any]) -> list[dict[str, Any]]

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/chat_adapter.py

def __call__(
    self,
    lm: BaseLM,
    lm_kwargs: dict[str, Any],
    signature: type[Signature],
    demos: list[dict[str, Any]],
    inputs: dict[str, Any],
) -> list[dict[str, Any]]:
    try:
        return super().__call__(lm, lm_kwargs, signature, demos, inputs)
    except Exception as e:
        # fallback to JSONAdapter
        from dspy.adapters.json_adapter import JSONAdapter

        if isinstance(e, LMError) or isinstance(self, JSONAdapter) or not self.use_json_adapter_fallback:
            # On LM errors, already using JSONAdapter, or use_json_adapter_fallback is False, we don't want to
            # retry with a different adapter. Raise the original error instead of the fallback error.
            raise
        return self._make_json_adapter_fallback()(lm, lm_kwargs, signature, demos, inputs)

acall(lm: BaseLM, lm_kwargs: dict[str, Any], signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any]) -> list[dict[str, Any]] async

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/chat_adapter.py

async def acall(
    self,
    lm: BaseLM,
    lm_kwargs: dict[str, Any],
    signature: type[Signature],
    demos: list[dict[str, Any]],
    inputs: dict[str, Any],
) -> list[dict[str, Any]]:
    try:
        return await super().acall(lm, lm_kwargs, signature, demos, inputs)
    except Exception as e:
        # fallback to JSONAdapter
        from dspy.adapters.json_adapter import JSONAdapter

        if isinstance(e, LMError) or isinstance(self, JSONAdapter) or not self.use_json_adapter_fallback:
            # On LM errors, already using JSONAdapter, or use_json_adapter_fallback is False, we don't want to
            # retry with a different adapter. Raise the original error instead of the fallback error.
            raise
        return await self._make_json_adapter_fallback().acall(lm, lm_kwargs, signature, demos, inputs)

format(signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any]) -> list[dict[str, Any]]

Format the input messages for the LM call.

This method converts the DSPy structured input along with few-shot examples and conversation history into multiturn messages as expected by the LM. For custom adapters, this method can be overridden to customize the formatting of the input messages.

In general we recommend the messages to have the following structure:

[
    {"role": "system", "content": system_message},
    # Begin few-shot examples
    {"role": "user", "content": few_shot_example_1_input},
    {"role": "assistant", "content": few_shot_example_1_output},
    {"role": "user", "content": few_shot_example_2_input},
    {"role": "assistant", "content": few_shot_example_2_output},
    ...
    # End few-shot examples
    # Begin conversation history
    {"role": "user", "content": conversation_history_1_input},
    {"role": "assistant", "content": conversation_history_1_output},
    {"role": "user", "content": conversation_history_2_input},
    {"role": "assistant", "content": conversation_history_2_output},
    ...
    # End conversation history
    {"role": "user", "content": current_input},
]

And system message should contain the field description, field structure, and task description.

Parameters:

Name	Type	Description	Default
signature	type[Signature]	The DSPy signature for which to format the input messages.	required
demos	list[dict[str, Any]]	A list of few-shot examples.	required
inputs	dict[str, Any]	The input arguments to the DSPy module.	required

Returns:

Type	Description
list[dict[str, Any]]	A list of multiturn messages as expected by the LM.

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/base.py

def format(
    self,
    signature: type[Signature],
    demos: list[dict[str, Any]],
    inputs: dict[str, Any],
) -> list[dict[str, Any]]:
    """Format the input messages for the LM call.

    This method converts the DSPy structured input along with few-shot examples and conversation history into
    multiturn messages as expected by the LM. For custom adapters, this method can be overridden to customize
    the formatting of the input messages.

    In general we recommend the messages to have the following structure:
    ```
    [
        {"role": "system", "content": system_message},
        # Begin few-shot examples
        {"role": "user", "content": few_shot_example_1_input},
        {"role": "assistant", "content": few_shot_example_1_output},
        {"role": "user", "content": few_shot_example_2_input},
        {"role": "assistant", "content": few_shot_example_2_output},
        ...
        # End few-shot examples
        # Begin conversation history
        {"role": "user", "content": conversation_history_1_input},
        {"role": "assistant", "content": conversation_history_1_output},
        {"role": "user", "content": conversation_history_2_input},
        {"role": "assistant", "content": conversation_history_2_output},
        ...
        # End conversation history
        {"role": "user", "content": current_input},
    ]

    And system message should contain the field description, field structure, and task description.
    ```


    Args:
        signature: The DSPy signature for which to format the input messages.
        demos: A list of few-shot examples.
        inputs: The input arguments to the DSPy module.

    Returns:
        A list of multiturn messages as expected by the LM.
    """
    inputs_copy = dict(inputs)

    # If the signature and inputs have conversation history, we need to format the conversation history and
    # remove the history field from the signature.
    history_field_name = self._get_history_field_name(signature)
    if history_field_name:
        # In order to format the conversation history, we need to remove the history field from the signature.
        signature_without_history = signature.delete(history_field_name)
        conversation_history = self.format_conversation_history(
            signature_without_history,
            history_field_name,
            inputs_copy,
        )

    messages = []
    system_message = self.format_system_message(signature)
    messages.append({"role": "system", "content": system_message})
    messages.extend(self.format_demos(signature, demos))
    if history_field_name:
        # Conversation history and current input
        content = self.format_user_message_content(signature_without_history, inputs_copy, main_request=True)
        messages.extend(conversation_history)
        if content:
            messages.append({"role": "user", "content": content})
    else:
        # Only current input
        content = self.format_user_message_content(signature, inputs_copy, main_request=True)
        if content:
            messages.append({"role": "user", "content": content})

    return [_expand_legacy_custom_type_markers_in_chat_message(message) for message in messages]

format_assistant_message_content(signature: type[Signature], outputs: dict[str, Any], missing_field_message=None) -> str

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/xml_adapter.py

def format_assistant_message_content(
    self,
    signature: type[Signature],
    outputs: dict[str, Any],
    missing_field_message=None,
) -> str:
    return self.format_field_with_value(
        {
            FieldInfoWithName(name=k, info=v): outputs.get(k, missing_field_message)
            for k, v in signature.output_fields.items()
        },
    )

format_conversation_history(signature: type[Signature], history_field_name: str, inputs: dict[str, Any]) -> list[dict[str, Any]]

Format the conversation history.

This method formats the conversation history and the current input as multiturn messages.

Parameters:

Name	Type	Description	Default
signature	type[Signature]	The DSPy signature for which to format the conversation history.	required
history_field_name	str	The name of the history field in the signature.	required
inputs	dict[str, Any]	The input arguments to the DSPy module.	required

Returns:

Type	Description
list[dict[str, Any]]	A list of multiturn messages as expected by the LM.

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/base.py

def format_conversation_history(
    self,
    signature: type[Signature],
    history_field_name: str,
    inputs: dict[str, Any],
) -> list[dict[str, Any]]:
    """Format the conversation history.

    This method formats the conversation history and the current input as multiturn messages.

    Args:
        signature: The DSPy signature for which to format the conversation history.
        history_field_name: The name of the history field in the signature.
        inputs: The input arguments to the DSPy module.

    Returns:
        A list of multiturn messages as expected by the LM.
    """
    conversation_history = inputs[history_field_name].messages if history_field_name in inputs else None

    if conversation_history is None:
        return []

    messages = []
    for message in conversation_history:
        tool_call_field_name, tool_calls = _tool_calls_from_message(message)
        tool_call_results = (
            ToolCallResults.model_validate(tool_calls.tool_call_results)
            if tool_calls is not None and tool_calls.tool_call_results is not None
            else None
        )

        user_content = self.format_user_message_content(signature, message)
        if user_content:
            messages.append({"role": "user", "content": user_content})

        if self.use_native_function_calling and tool_calls is not None:
            content_signature = signature
            for name, field in signature.output_fields.items():
                if field.annotation == ToolCalls or message.get(name) is None:
                    content_signature = content_signature.delete(name)

            content = (
                self.format_assistant_message_content(content_signature, message)
                if content_signature.output_fields
                else ""
            )

            if tool_call_results is not None:
                tool_call_ids = [tool_call.id for tool_call in tool_calls.tool_calls]
                result_ids = [result.call_id for result in tool_call_results.tool_call_results]
                if tool_call_ids != result_ids or not all(tool_call_ids):
                    tool_call_results = None

            if content or tool_call_results is not None:
                assistant_message: dict[str, Any] = {"role": "assistant", "content": content or None}
                if tool_call_results is not None:
                    assistant_message["tool_calls"] = [
                        _tool_call_as_openai_message_tool_call(tool_call) for tool_call in tool_calls.tool_calls
                    ]
                messages.append(assistant_message)

            if tool_call_results is not None:
                for result in tool_call_results.tool_call_results:
                    content = _tool_result_content(result.value)
                    messages.append(
                        {"role": "tool", "tool_call_id": result.call_id, "name": result.name, "content": content}
                    )
            continue

        assistant_values = message
        if tool_call_field_name is not None and tool_call_results is not None:
            assistant_values = dict(message)
            assistant_values[tool_call_field_name] = tool_calls.model_copy(update={"tool_call_results": None})

        assistant_content = self.format_assistant_message_content(signature, assistant_values)
        if assistant_content:
            messages.append({"role": "assistant", "content": assistant_content})
        if tool_call_results is not None:
            result_input = {"tool_call_results": tool_call_results}
            content = self.format_user_message_content(_TOOL_CALL_RESULTS_SIGNATURE, result_input)
            messages.append({"role": "user", "content": content})

    # Remove the history field from the inputs
    del inputs[history_field_name]

    return messages

format_demos(signature: type[Signature], demos: list[dict[str, Any]]) -> list[dict[str, Any]]

Format the few-shot examples.

This method formats the few-shot examples as multiturn messages.

Parameters:

Name	Type	Description	Default
signature	type[Signature]	The DSPy signature for which to format the few-shot examples.	required
demos	list[dict[str, Any]]	A list of few-shot examples, each element is a dictionary with keys of the input and output fields of the signature.	required

Returns:

Type	Description
list[dict[str, Any]]	A list of multiturn messages.

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/base.py

def format_demos(self, signature: type[Signature], demos: list[dict[str, Any]]) -> list[dict[str, Any]]:
    """Format the few-shot examples.

    This method formats the few-shot examples as multiturn messages.

    Args:
        signature: The DSPy signature for which to format the few-shot examples.
        demos: A list of few-shot examples, each element is a dictionary with keys of the input and output fields of
            the signature.

    Returns:
        A list of multiturn messages.
    """
    complete_demos = []
    incomplete_demos = []

    for demo in demos:
        # Check if all fields are present and not None
        is_complete = all(k in demo and demo[k] is not None for k in signature.fields)

        # Check if demo has at least one input and one output field
        has_input = any(k in demo for k in signature.input_fields)
        has_output = any(k in demo for k in signature.output_fields)

        if is_complete:
            complete_demos.append(demo)
        elif has_input and has_output:
            # We only keep incomplete demos that have at least one input and one output field
            incomplete_demos.append(demo)

    messages = []

    incomplete_demo_prefix = "This is an example of the task, though some input or output fields are not supplied."
    for demo in incomplete_demos:
        messages.append(
            {
                "role": "user",
                "content": self.format_user_message_content(signature, demo, prefix=incomplete_demo_prefix),
            }
        )
        messages.append(
            {
                "role": "assistant",
                "content": self.format_assistant_message_content(
                    signature, demo, missing_field_message="Not supplied for this particular example. "
                ),
            }
        )

    for demo in complete_demos:
        messages.append({"role": "user", "content": self.format_user_message_content(signature, demo)})
        messages.append(
            {
                "role": "assistant",
                "content": self.format_assistant_message_content(
                    signature, demo, missing_field_message="Not supplied for this conversation history message. "
                ),
            }
        )

    return messages

format_field_description(signature: type[Signature]) -> str

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/chat_adapter.py

def format_field_description(self, signature: type[Signature]) -> str:
    return (
        f"Your input fields are:\n{get_field_description_string(signature.input_fields)}\n"
        f"Your output fields are:\n{get_field_description_string(signature.output_fields)}"
    )

format_field_structure(signature: type[Signature]) -> str

XMLAdapter requires input and output fields to be wrapped in XML tags like <field_name>.

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/xml_adapter.py

def format_field_structure(self, signature: type[Signature]) -> str:
    """
    XMLAdapter requires input and output fields to be wrapped in XML tags like `<field_name>`.
    """

    parts = []
    parts.append("All interactions will be structured in the following way, with the appropriate values filled in.")

    def format_signature_fields_for_instructions(fields: dict[str, FieldInfo]):
        return self.format_field_with_value(
            fields_with_values={
                FieldInfoWithName(name=field_name, info=field_info): translate_field_type(field_name, field_info)
                for field_name, field_info in fields.items()
            },
        )

    parts.append(format_signature_fields_for_instructions(signature.input_fields))
    parts.append(format_signature_fields_for_instructions(signature.output_fields))
    return "\n\n".join(parts).strip()

format_field_with_value(fields_with_values: dict[FieldInfoWithName, Any]) -> str

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/xml_adapter.py

def format_field_with_value(self, fields_with_values: dict[FieldInfoWithName, Any]) -> str:
    output = []
    for field, field_value in fields_with_values.items():
        formatted = format_field_value(field_info=field.info, value=field_value)
        output.append(f"<{field.name}>\n{formatted}\n</{field.name}>")
    return "\n\n".join(output).strip()

format_finetune_data(signature: type[Signature], demos: list[dict[str, Any]], inputs: dict[str, Any], outputs: dict[str, Any]) -> dict[str, list[Any]]

Format the call data into finetuning data according to the OpenAI API specifications.

For the chat adapter, this means formatting the data as a list of messages, where each message is a dictionary with a “role” and “content” key. The role can be “system”, “user”, or “assistant”. Then, the messages are wrapped in a dictionary with a “messages” key.

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/chat_adapter.py

def format_finetune_data(
    self,
    signature: type[Signature],
    demos: list[dict[str, Any]],
    inputs: dict[str, Any],
    outputs: dict[str, Any],
) -> dict[str, list[Any]]:
    """
    Format the call data into finetuning data according to the OpenAI API specifications.

    For the chat adapter, this means formatting the data as a list of messages, where each message is a dictionary
    with a "role" and "content" key. The role can be "system", "user", or "assistant". Then, the messages are
    wrapped in a dictionary with a "messages" key.
    """
    system_user_messages = self.format(  # returns a list of dicts with the keys "role" and "content"
        signature=signature, demos=demos, inputs=inputs
    )
    assistant_message_content = self.format_assistant_message_content(  # returns a string, without the role
        signature=signature, outputs=outputs
    )
    assistant_message = {"role": "assistant", "content": assistant_message_content}
    messages = system_user_messages + [assistant_message]
    return {"messages": messages}

format_system_message(signature: type[Signature]) -> str

Format the system message for the LM call.

Parameters:

Name	Type	Description	Default
signature	type[Signature]	The DSPy signature for which to format the system message.	required

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/base.py

def format_system_message(self, signature: type[Signature]) -> str:
    """Format the system message for the LM call.


    Args:
        signature: The DSPy signature for which to format the system message.
    """
    return (
        f"{self.format_field_description(signature)}\n"
        f"{self.format_field_structure(signature)}\n"
        f"{self.format_task_description(signature)}"
    )

format_task_description(signature: type[Signature]) -> str

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/chat_adapter.py

def format_task_description(self, signature: type[Signature]) -> str:
    instructions = textwrap.dedent(signature.instructions)
    objective = ("\n" + " " * 8).join([""] + instructions.splitlines())
    return f"In adhering to this structure, your objective is: {objective}"

format_user_message_content(signature: type[Signature], inputs: dict[str, Any], prefix: str = '', suffix: str = '', main_request: bool = False) -> str

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/xml_adapter.py

def format_user_message_content(
    self,
    signature: type[Signature],
    inputs: dict[str, Any],
    prefix: str = "",
    suffix: str = "",
    main_request: bool = False,
) -> str:
    messages = [prefix]

    messages.append(self.format_field_with_value(
        {
            FieldInfoWithName(name=k, info=v): inputs.get(k)
            for k, v in signature.input_fields.items() if k in inputs
        },
    ))

    if main_request:
        output_requirements = self.user_message_output_requirements(signature)
        if output_requirements is not None:
            messages.append(output_requirements)

    messages.append(suffix)
    return "\n\n".join(messages).strip()

parse(signature: type[Signature], completion: str) -> dict[str, Any]

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/xml_adapter.py

def parse(self, signature: type[Signature], completion: str) -> dict[str, Any]:
    fields = {}
    for match in self.field_pattern.finditer(completion):
        name = match.group("name")
        content = match.group("content").strip()
        if name in signature.output_fields and name not in fields:
            fields[name] = content
    # Cast values using base class parse_value helper
    for k, v in fields.items():
        fields[k] = self._parse_field_value(signature.output_fields[k], v, completion, signature)
    if fields.keys() != signature.output_fields.keys():
        from dspy.utils.exceptions import AdapterParseError

        raise AdapterParseError(
            adapter_name="XMLAdapter",
            signature=signature,
            lm_response=completion,
            parsed_result=fields,
        )
    return fields

user_message_output_requirements(signature: type[Signature]) -> str

Source code in .venv/lib/python3.14/site-packages/dspy/adapters/xml_adapter.py

def user_message_output_requirements(self, signature: type[Signature]) -> str:
    message = "Respond with the corresponding output fields wrapped in XML tags "
    message += ", then ".join(f"`<{f}>`" for f in signature.output_fields)
    message += "."
    return message